Systems and methods for leveraging social queuing to identify and prevent ticket purchaser simulation

ABSTRACT

A method for identifying a simulated social media account history is provided. The method may include querying a social media account to obtain social media identification information. The querying may determine whether the account history includes one or more parameters that indicate whether the social media account is related to an automated entity or a human entity. The parameters may include at least one of less than a threshold number of friends on the account; more than a threshold frequency of historic ticket purchases per unit time; disparate location of historic ticket purchases per unit time and/or a historic record of less than a threshold reaction time to a plurality of ticket offers.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation application of U.S. application Ser.No. 16/132,584, filed Sep. 17, 2018, which is a continuation applicationwhich claims the benefit of U.S. application Ser. No. 14/595,840, filedJan. 13, 2015, now U.S. Pat. No. 10,078,851, issued Sep. 17, 2018, ofwhich is assigned to the assignee hereof, and of which the disclosuresare incorporated herein by reference in their entirety for all purposes.

FIELD OF TECHNOLOGY

The disclosure relates to prioritizing potential event ticketpurchasers. This disclosure also relates to simulating, identifyingand/or searching for potential event ticket purchaser behavior.

BACKGROUND OF THE DISCLOSURE

Currently, ticket resellers utilize automated software for purchasingtickets. Such automated software may, in turn, utilize applicationprogramming interface(s) (“API(s)”) to access event ticket purchasingwebsites. These APIs typically are supported by one or more computers.

APIs are to software and/or hardware information systems as plug andsocket designs are to appliances, such as lamps. More specifically, APIsallows software and/or hardware information systems to interoperate withother systems and/or programs, in a way similar to the way that plugsand sockets allow lamps to operate with the electrical grid. Just as aplug conforms to the contours of a socket in order for electricity toflow to allow the lamp to operate, so to a computer program designed tobe compatible with another program should preferably conform to the APIof the first program. In other words, the API of the first program mayestablish rules about how other programs can send and receiveinformation so that the two programs can work together to executespecific tasks.

Programs for selling event tickets are typically designed for humaninteraction—i.e., these programs are designed to be used by human ticketpurchasers. However, ticket resellers have created applications withautomated software that utilizes APIs designed to interact with theprograms for selling event tickets. Such applications, and APIsassociated therewith, may enjoy an unfair advantage over human ticketpurchasers. The unfair advantage may result, at least in part, becausethe reaction time of the applications created by resellers, is typicallyless than the reaction time of human ticket purchasers, and the volumeof ticket purchasing attempts made by resellers is typically more thanthe volume of attempts made by human ticket purchasers. Nevertheless,such applications typically exhibit automated computer-like behavior.

It would be desirable to provide event ticket systems and methods thatreduce the unfair advantage in ticket purchases enjoyed by automatedticket purchasing software and associated APIs.

SUMMARY OF THE DISCLOSURE

Certain embodiments may include an event ticket distribution system. Thesystem may include a receiver configured to receive a plurality oflogins.

Each of the logins may be intended for an event ticket purchase. Each ofthe logins may include, or be associated with, social mediaidentification information (“social media ID”). In certain embodiments,more than one login may be associated with a single user to allowsystems and methods according to the invention to retrieve accounthistory from a number of different of social media sites associated withthe user.

The system may include a processor configured to retrieve accounthistory associated with the social media ID. The processor may befurther configured to calculate an index value associated with thesocial media ID. The index value may be based, at least in part, on theretrieved account history.

When the index value associated with a login is above a pre-determinedthreshold value, the processor may be further configured to assign apriority flag to the login. The priority flag may enable a preferablyreal-time event ticket purchase not available in real-time, or, incertain embodiments, not available at all, to a non-flagged login. Itshould be noted that, to obtain priority treatment on a login topurchase tickets, the purchaser should, at least in some embodiments,pre-register in order to have their requested ticket purchase bequalified as a priority purchase. In such circumstances, the purchasermay receive priority ticket purchase opportunities in real-time, whereassuch real-time ticket purchase opportunities may not be available to anon-flagged login. It should be noted that, in certain embodiments, thepre-registration step preferably provides time to retrieve and analyze auser's social media history. Such analysis may preferably provide adetailed understanding of the relative “value” of the user to theperforming entity. In certain embodiments, such a value determinationmay preferably dictate a timed (where users are allotted prioritizedtimes), staggered (where users receive ordered times relative to oneanother), windowed (where users are provided time windows) or othersuitably configured system for interacting with, searching for, and/orpurchasing of tickets.

BRIEF DESCRIPTION OF THE DRAWINGS

The objects and advantages of the invention will be apparent uponconsideration of the following detailed description, taken inconjunction with the accompanying drawings, in which like referencecharacters refer to like parts throughout, and in which:

FIG. 1 shows illustrative apparatus in accordance with the principles ofthe invention;

FIG. 2 shows another illustrative apparatus in accordance with theprinciples of the invention;

FIG. 3 shows an illustrative flow diagram of methods according tocertain embodiments;

FIG. 4 shows an illustrative flow diagram of information extraction anddata mining according to certain embodiments;

FIG. 5 shows an illustrative flow diagram of index generation accordingto certain embodiments;

FIG. 6 shows an illustrative flow diagram of filtering and sortingticket purchasing opportunities based on the index;

FIG. 7 shows an illustrative hybrid flow diagram of filtering andsorting ticket purchasing opportunities based on the index with eventseating maps; and

FIG. 8 shows an illustrative flow diagram of filtering and sortingticket purchasing opportunities based on the index.

DETAILED DESCRIPTION OF THE DISCLOSURE

In some embodiments, an entity may have an interest in populatingselected seats for an event with selected participants, such asentity-loyal participants. Such an entity may choose to award ticketpurchasing priority to certain entity-loyal participants based onidentifiable indications of loyalty associated with the social media IDsor other activity identifying information or websites of suchentity-loyal participants. This process may be referred to herein as“social queuing.” For the purposes of this application, “social queuing”may be understood to be prioritizing entity-loyal event participants inthe ticket purchasing queue based on account history corresponding tosocial media IDs. Social queuing may also include limiting access, ordenying access to certain participants based on account historycorresponding to social media IDs or other suitable indicia. Inaddition, social queuing may be based on activity identifyinginformation obtained from websites and/or apps such as Songkick™,iTunes™, Amazon™, YouTube™, or other such applications that track and/ormonitor user affinity to select performers. Information obtained fromsuch applications may be preferable at least because such information istypically pre-screened to insure the integrity of the information.

In embodiments involving such apps, a user may preferably authorize suchembodiments to inspect and retrieve information resident in apps and/orwebsites and then, based on the information obtained therefrom, notifythe user regarding upcoming performances in the user's geographic area.Furthermore, such embodiments may preferably provide ticket purchasingopportunities to similar shows or performances. It should be noted thatsuch embodiments may also be used for tracking user interest in moviesor other such performances and notifying and/or offering tickets to suchperformances based upon the tracking.

It should be noted further that such embodiments may also be utilizedfor determining historical commitment to charitable or commercialorganizations or outlets, based, at least in part on dollars contributedor spent over time and/or historical attendance at affairs associatedtherewith.

Furthermore, such an entity may decide to limit ticket resellers, theirsoftware and their associated APIs, from becoming priority event ticketpurchasers. Accordingly, such an entity may decide to limit the ticketselection of resellers, their software and their associated APIs.

Illustrative embodiments of apparatus and methods in accordance with theprinciples of the invention will now be described with reference to theaccompanying drawings, which form a part hereof. It is to be understoodthat other embodiments may be utilized and structural, functional andprocedural modifications may be made without departing from the scopeand spirit of the present invention.

As will be appreciated by one of skill in the art upon reading thefollowing disclosure, the embodiments may be embodied as a method, adata processing system, or a computer program product. Accordingly, theembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment combining software andhardware aspects.

Furthermore, embodiments may take the form of a computer program productstored by one or more computer-readable storage media havingcomputer-readable program code, or instructions, embodied in or on thestorage media. Any suitable computer readable storage media may beutilized, including hard disks, CD-ROMs, optical storage devices,magnetic storage devices, and/or any combination thereof. In addition,various signals representing data or events as described herein may betransferred between a source and a destination in the form ofelectromagnetic waves traveling through signal-conducting media such asmetal wires, optical fibers, and/or wireless transmission media (e.g.,air and/or space).

Exemplary embodiments may be embodied at least partially in hardware andinclude one or more databases, receivers, transmitters, processors,modules including hardware and/or any other suitable hardware.Furthermore, operations executed may be performed by the one or moredatabases, receivers, transmitters, processors and/or modules includinghardware.

FIG. 1 is a block diagram that illustrates a generic computing device101 (alternately referred to herein as a “server”) that may be usedaccording to an illustrative embodiment of the invention. The computerserver 101 may have a processor 103 for controlling overall operation ofthe server and its associated components, including RAM 105, ROM 107,input/output module 109, and memory 115.

Input/output (“I/O”) module 109 may include a microphone, keypad, touchscreen, and/or stylus through which a user of server 101 may provideinput, and may also include one or more of a speaker for providing audiooutput and a video display device for providing textual, audiovisualand/or graphical output. Software may be stored within memory 115 and/orstorage to provide instructions to processor 103 for enabling server 101to perform various functions. For example, memory 115 may store softwareused by server 101, such as an operating system 117, applicationprograms 119, and an associated database 111. Alternately, some or allof server 101 computer executable instructions may be embodied inhardware or firmware (not shown). As described in detail below, database111 may provide storage for social media information, social mediaidentification information, one or more algorithms for use withgenerating an index according to certain embodiments, existing ticketopportunities, etc.

Server 101 may operate in a networked environment supporting connectionsto one or more remote computers, such as terminals 141 and 151.Terminals 141 and 151 may be personal computers or servers that includemany or all of the elements described above relative to server 101. Thenetwork connections depicted in FIG. 1 include a local area network(LAN) 125 and a wide area network (WAN) 129, but may also include othernetworks. When used in a LAN networking environment, computer 101 isconnected to LAN 125 through a network interface or adapter 113. Whenused in a WAN networking environment, server 101 may include a modem 127or other means for establishing communications over WAN 129, such asInternet 131. It will be appreciated that the network connections shownare illustrative and other means of establishing a communications linkbetween the computers may be used. The existence of any of variouswell-known protocols such as TCP/IP, Ethernet, FTP, HTTP and the like ispresumed, and the system can be operated in a client-serverconfiguration to permit a user to retrieve web pages via the World WideWeb from a web-based server. Any of various conventional web browserscan be used to display and manipulate data on web pages.

Additionally, application program 119, which may be used by server 101,may include computer executable instructions for invoking userfunctionality related to communication, such as email, short messageservice (SMS), and voice input and speech recognition applications.

Computing device 101 and/or terminals 141 or 151 may also be mobileterminals including various other components, such as a battery,speaker, and antennas (not shown).

A terminal such as 141 or 151 may be used by a user of the embodimentsset forth herein. Information input may be stored in memory 115. Theinput information may be processed by an application such as one ofapplications 119.

FIG. 2 shows an illustrative apparatus that may be configured inaccordance with the principles of the invention.

FIG. 2 shows illustrative apparatus 200. Apparatus 200 may be acomputing machine. Apparatus 200 may be included in apparatus shown inFIG. 1. Apparatus 200 may include chip module 202, which may include oneor more integrated circuits, and which may include logic configured toperform any other suitable logical operations.

Apparatus 200 may include one or more of the following components: I/Ocircuitry 204, which may include the transmitter device and the receiverdevice and may interface with fiber optic cable, coaxial cable,telephone lines, wireless devices, PHY layer hardware, a keypad/displaycontrol device or any other suitable encoded media or devices;peripheral devices 206, which may include counter timers, real-timetimers, power-on reset generators or any other suitable peripheraldevices; logical processing device (“processor”) 208, which may computedata structural information, structural parameters of the data, quantifyindices; and machine-readable memory 210.

Machine-readable memory 210 may be configured to store inmachine-readable data structures: social media information, social mediaidentification information, one or more algorithms for use withgenerating an index according to certain embodiments, existing ticketopportunities, and/or any other suitable information or data structures.

Components 202, 204, 206, 208 and 210 may be coupled together by asystem bus or other interconnections 212 and may be present on one ormore circuit boards such as 220. In some embodiments, the components maybe integrated into a single silicon-based chip.

Apparatus 200 may operate in a networked environment supportingconnections to one or more remote computers via a local area network(LAN), a wide area network (WAN), or other suitable networks. When usedin a LAN networking environment, apparatus 200 may be connected to theLAN through a network interface or adapter in I/O circuitry 204. Whenused in a WAN networking environment, apparatus 200 may include a modemor other means for establishing communications over the WAN. It will beappreciated that the network connections shown are illustrative andother means of establishing a communications link between the computersmay be used. The existence of any of various well-known protocols suchas TCP/IP, Ethernet, FTP, HTTP and the like is presumed, and the systemmay be operated in a client-server configuration to permit a user tooperate processor 208, for example over the Internet.

Apparatus 200 may be included in numerous general purpose or specialpurpose computing system environments or configurations. Examples ofwell-known computing systems, environments, and/or configurations thatmay be suitable for use with the invention include, but are not limitedto, personal computers, server computers, hand-held or laptop devices,mobile phones and/or other personal digital assistants (“PDAs”),multiprocessor systems, microprocessor-based systems, tablets,programmable consumer electronics, network PCs, minicomputers, mainframecomputers, distributed computing environments that include any of theabove systems or devices, and the like.

A ticket distribution system according to some embodiments may include areceiver. The receiver may be configured to receive a plurality oflogins. Each of the logins may be intended for an event ticket purchase.Each of the logins may include an e-mail address or other electronicinformation associated with a user. Each of the logins may includesocial media identification information such as social media IDs. Eachof the logins preferably facilitates the obtaining of account historyassociated with social media IDs or other suitable history relating tothe user.

The system may also include a processor. The processor may be configuredto retrieve account history associated with the social media ID. Theretrieval of account history may preferably occur pursuant to apre-registration by an event ticket purchaser. The pre-registration mayoccur at some pre-determined time prior to when the tickets are offeredfor sale.

Such a pre-registration may preferably grant access to a system toretrieve substantially all (or most, or some) information from theaccount history of the social media ID. Further, such a pre-registrationmay allow the system to further retrieve related information from othersocial media sites linked to the social media site to which the systemhas been granted access. Such a pre-registration may allow the system tofurther retrieve related information from other general sites, such as,for example, Spotify™, a music-downloading site, linked to the socialmedia site to which the system has been granted access.

The processor may be further configured to calculate an index valueassociated with the login and/or other identifying information. Theindex value may be based, at least in part, on the retrieved accounthistory. The index value may be based, at least in part, on theretrieved information from sites related, or otherwise linked, to thesocial media site.

When the index value associated with a login is above (or below, inother embodiments) a pre-determined threshold value, the processor maybe further configured to assign a priority flag to the login. Thepriority flag may enable a real-time ticket event purchase not availablein real-time to a non-flagged login. Other benefits awarded to apriority login are also within the scope of this invention.

The index value may also be based, at least in part, on other historicalinformation such as, for example, historical ticket purchases forperformances that the user attended. Such attendance at performances maybe verified information obtained from credit card, debit card, or othersuitable payment instrument, activity at the performances; governmentidentification instruments provided at performances; apps that areresident on a person's mobile device that are used to process and/orpresent tickets at a performance and can verify attendances thereat;and/or other third party information which can be used to verifyhistorical performance attendance.

The account history may include at least one preferably time-stampedindication of affinity for an entity, such as a performing entity,associated with the event. The account history may include at least oneoccurrence of a tagging of a photograph of an entity associated with theevent.

The account history may also include at least one occurrence of alinking of a photograph of a performing entity associated with the eventto the profile of the social media ID. The linking of the photograph ofthe performing entity associated with the event to the profile of thesocial media ID may be the same as, or different from, the tagging of aphotograph of the performing entity associated with the event to theprofile of the social media ID.

The account history may also include an indication of a threshold amountof streaming audio time related to a performing entity associated withthe event. Such streaming audio time may preferably be obtained byaccessing, via the social media ID, linked account(s) at a streamingaudio website, and downloading, therefrom, streaming audio information.

The account history may also include one or more parameters thatindicate whether the social media ID is related to an automated entityor a human entity. The parameters may include at least one ofpre-determined number of friends on the account. The parameters mayinclude a pre-determined ticket purchasing frequency. The parameters mayinclude a location or quantity of historic ticket purchases and historicreaction time to ticket offers or other suitable indicia of ticketreselling history. In certain embodiments, the parameters may include adetermination as to whether the social media ID is related to an entitythat has a history of ticket reselling. Such an entity, or ID, maypreferably be flagged or placed on a watch list for future attempts topurchase tickets.

Embodiments may include a method for simulating a social media accounthistory. The method may include generating a social media ID or using analready existent social media ID.

Embodiments of methods may also include creating an account history forthe social media ID with respect to a pre-determined entity. Thepre-determined entity may be the entity associated with the event. Thetickets may provide an opportunity to view the entity at the event.

The creating may include using the social media ID to create at leastone time-stamped indication of affinity for the pre-determined entity.The creating may include using the social media ID to tag a photographof the entity. The method may also include linking the social media IDto a photograph of the pre-determined entity. The method may alsoinclude using the social media ID to store a threshold amount ofstreaming audio time relating to the pre-determined entity.

Historical purchase(s) of merchandise, such as t-shirts, mugs, or otherperformer associated paraphernalia, may also indicate an affinity forthe pre-determined entity. In some embodiments, using time stampsassociated with such purchase(s) of merchandise to indicate affinity forthe pre-determined entity may provide deeper insight into the historicaltendencies of users.

The method may further include querying the account history to determinewhether the account history includes one or more parameters thatindicate whether the social media ID is related to an automated entityor a human entity.

For example, if it is determined that the social media ID includes lessthan a threshold number of friends on the account, then this may be anindication that the social media ID is related to an automated entity.Alternatively, if it is determined that the social media ID includesmore than a threshold frequency of historic ticket purchases per unittime then it may be determined that the social media ID is related to anautomated entity.

In yet another alternative embodiment, if it is determined that thesocial media ID has purchased tickets for events in geographicallydisparate locations of historic ticket purchases, and, in someembodiments, the events associated with such ticket purchases take placewithin a certain amount of time, then this may be an indication that thesocial media ID is related to an automated entity. Yet anotherindication that the social media ID is related to an automated entity isthat the entity may include a historic record of more or less than athreshold reaction time to a plurality of ticket offers.

FIG. 3 shows an illustrative flow diagram of methods according tocertain embodiments. Step 302 shows receiving e-mail addresses,Facebook™, third party data source identification information (such asidentification information for logins and/or passwords to Songkick™,iTunes™, Amazon™) or other suitable media, identification (“ID”) numbersassociated therewith and/or one or more other social media IDs. Suchinformation may be received, for example, from logins to an entity's(such as a music band, a solo performer, a sports team, a theatricalproduction, a particular venue, a particular brand in support of aseries of performances such as the Budweiser™ Made in America Festival™series or other suitable entity, venue or brand) web site (while much ofthe disclosure herein relates to performers or bands, nevertheless itshould be understood that the disclosure relates as well to any suitableperformer, venue, band or entity related thereto). In one embodiment,the entity's web site may preferably support a platform for receivingparticipant logins. Such a platform may preferably provide a place toenter a social media ID. Such a platform may preferably allow fortransfer of such social media IDs to a centralized data storage locationsuch that the social media IDs can be fully inspected at a later date.

Step 304 shows receiving authorization from users to search informationcorresponding to the social media IDs or third party data sourceidentification information. Authorization information is preferablylogged with the third party and used in accordance with the thirdparty's own terms and conditions. Such authorization may preferablyinclude password information and/or other information necessary toenable access to the participant's accounts.

Step 306 shows, preferably using the enabled access to the user'saccounts, obtaining relevant information from participants' social mediaaccounts or third party data source identification information. Suchrelevant information may include transactions, posts, downloads, e-mailaddresses and any other suitable stored information on the participant'ssocial media accounts or third party data source identificationinformation.

In certain embodiments, the user may restrict access to certain portionsof the social media or third party data source identificationinformation. For example, the user may select areas of his social mediaor third party data source identification information to which theaccess is limited.

In certain embodiments, access to the user's social media accounts orthird party data source identification information may be obtained usingcookies, or tracking cookies, to follow and store the user's internetnavigation. As a general matter, tracking cookies may be used to trackinternet users' web browsing.

Such cookies, or tracking cookies, may preferably be used with theconsent of the users. In view of the foregoing, it should be noted thatinformation may be obtained from users either by the users providing theinformation or by following user navigational behavior.

In instances where the user's navigation is being tracked, certainembodiments may include offering the user predetermined event ticketpurchase opportunities preferably at select times. Such opportunitiesmay preferably be provided to the user during the user's navigation.Such opportunities may preferably correspond to the user experienceduring the user's navigation.

For example, when a user navigates to Spotify™ and streams a performer'smusic and is in the process of listening to the music, the embodimentsmay, preferably in real time, electronically notify the user of an eventticket purchase opportunity. Such notification may preferably allow theuser to immediately access the event ticket purchase opportunity. Suchnotification may preferably be sent by e-mail, text, using a website,using an app associated with third party data source identificationinformation and/or posting to a user's social media account (such as aFacebook™ account or other suitable account.) Such a notification may beshown on a screen of a device, transmitted as an audio advertisement inthe app., browser or other software interface in which Spotify™ is beingused. Such notification may be sent by multiple transmission channels.In certain embodiments, such a transmission may preferably include atermination time whereby the ability to exercise the event ticketpurchase opportunity may terminate at some pre-determined time in thefuture following the transmission of the notification.

Step 306 may also preferably including data mining the extractedinformation to determine participant tendencies such as affinity for oneor more performing entities, ticket purchasing frequency, geographiclocation of ticket purchasing, number of friends, etc. Data mining mayinclude using data from participant visits to web sites hosted bysystems according to the invention. Data mining may include using datafrom participant visits to web sites hosted by systems in communicationwith systems according to the invention. Data mining may include usingdata from cookies, such as tracking cookies, or other such mechanisms tobetter understand participant web navigation and, thereby, to betterunderstand participant tendencies. Data mining may include using datafrom third party data sources such as certain apps.

An example of data mining according to certain embodiments may includeidentifying tendencies of certain individuals over time. Once certainindividuals have been identified by, for example, systems and methods ofembodiments described herein, such individuals may be targeted andoffered ticket purchase opportunities ordinarily not available tonon-identified individuals. One component of such identification may bea dynamic register of time spent on a music streaming site listening tothe music of a pre-determined performer. When a particular individual isidentified as listening to a performer on a music downloading site at apre-determined high threshold rate of music per unit time, or more thana pre-determined high threshold amount, then such an individual may beoffered ticket purchase opportunities for the performer typically notavailable to non-identified individuals.

Another example of data mining may including using identified tendenciesfrom third party data sources such as Songkick™, iTunes™, and/or Amazon™or other suitable third party data sources.

Step 308 shows index generation. For each social media ID, such indexgeneration may include performing one or more algorithms to obtain apreferably dynamic index value. Such a dynamic index value may beupdated in response to changes to the participant's social media ID(which would preferably retrieve information from the social media on areal-time, or substantially real-time, basis). In some embodiments, sucha dynamic index value may preferably be adjusted in response to activeindex factors.

Such active index factors may include a determination that theparticipant has recently contributed to one or more charities on a list.In certain embodiments, a determination that a participant hascontributed to one or more charities on a list may be used to increasethe index score of an interested participant with respect to apredetermined event and/or entity. Such factors may include adetermination that a participant has executed one or more tweet(s) orother social media action regarding an event. Tickets for such an eventmay be currently, or imminently, offered for sale. In certainembodiments, a determination that a participant has executed one or moretweet regarding an event may be used to increase the index score of aninterested participant. In certain embodiments, the number of executionof tweets may obtain a proportional effect on the index score. Moretweets, or a higher rate of tweets per unit time, or other suitablemetric, may preferably obtain a greater effect on the index score.

In some embodiments, only non-triggered tweets, non-reward-based tweetsor tweets in a non-reward-based channel may preferably obtain an effecton an index score. Non-triggered tweets may include tweets that are notresponsive to a previous in time promotional advertisement requestingtweets or similar behavior. Non-reward-based tweets may include tweetsthat are not responsive to a previous in time reward program thatrewards users who execute such tweets. Tweets in a non-reward-basedchannel may include tweets executed in a channel that is not identifiedin a rewards program instituted by a performer, brand, or other suitableentity.

In certain embodiments, such a dynamic index value may be updatedperiodically, preferably on a system-set schedule. In some embodiments,the dynamic index value may be updated according to a user-definedschedule. Such an index may preferably reflect a history of theparticipant with respect to the entity seeking the information.

Step 310 shows filtering and sorting indexed social media IDs or thirdparty data source identification information. Such filtering and sortingmay be executed prior to offering for sale tickets to an event. Thefiltering of social media IDs or third party data source identificationinformation may be performed to eliminate, or limit, purchasing rightsof certain sub-optimal ticket purchasers such as potential ticketresellers. As mentioned above, such potential resellers may preferablyprovide automated software utilizing APIs (or not utilizing APIs) forattempting to purchase tickets. Such potential ticket resellers aretypically less desirable because they may resell the tickets independentof consideration of the respective loyalty to the performer of the endpurchasers. Instead, such potential ticket resellers may only considerthe end purchasers who can pay the highest price for the tickets insteadof the end purchasers who can pay only a lower price but, in view oftheir history, may be considered more devoted fans of the performer.

The sorting of the indexed social media IDs may be performed to increasethe likelihood that more dedicated, loyal and/or committed participantsmay be awarded tickets to a predetermined event.

In certain embodiments, when tickets are actually offered for sale,potential ticket purchasers with relatively higher index scores mayreceive higher priority in the form of a better chance of receivingticket purchasing opportunities as well as more desirable tickets—e.g.,with more desirable locations. In some embodiments, other incentivesoffered to loyal participants may include V.I.P. upgrades for tickets,backstage access, meet and greet opportunities, discounted merchandise,variable and preferred ticket pricing and/or other incentives for higherindex scores.

FIG. 4 shows an illustrative flow diagram of information extraction anddata mining according to certain embodiments. Step 406 shows obtainingrelevant information from social media accounts and/or third party datasources. Step 406 also shows the optional setting of a threshold indexvalue. A threshold index value may preferably enable a determination asto whether a potential ticket purchaser qualifies for a pre-determinedpriority ticket purchasing opportunity. The index value may be formedfrom answers to the queries set forth in steps 408, 410 and 412 as wellas the analysis performed in step 414.

Step 408 may query the available social media history to determinewhether the potential ticket purchaser's social media history indicatesthat he or she has an affinity for the entity. The affinity may bedetermined by a finding of statements such as “I love this entity,” orother such similar statements which juxtapose words of affection withina certain number of words of the entity.

Step 408 also shows that the timeliness of the statement of affinity mayalso affect the index value, and/or the identity determination, of thepotential ticket purchaser. For example, step 408 shows that the timingof the statement of affinity may form part of the analysis to determinewhether the potential ticket purchaser has a current and active affinityfor the band or other suitable performer.

Step 410 shows another possible factor for determining the level of thepotential ticket purchaser interest in the entity, such as a band.Specifically, step 410 shows determining whether the participant hastagged photos of the band, as is known in the art of social media, orphotos related to the band. Such tagging preferably indicates a level ofaffinity for the band using a different metric than the heretoforedescribed metrics.

Step 412 shows yet another possible factor for determining the level ofpotential ticket purchaser interest in the band. Specifically, step 412shows using the social media ID and/or third party data sources thatmonitor fan activity and/or identify fan trends to determine how oftenthe participant has used his or her streaming audio account, such as astreaming audio account on Spotify™, to listen to the band, or how oftenthe fan has visited a particular performers website. Alternatively, adata source app which tracks user affinity may also be exploited formonitoring both the user affinity and/or performance concert plans forperformers in which the user has expressed an interest. Such anembodiment may preferably enable a system to provide ticket purchasingopportunities to select users. Furthermore, such use of a streamingaudio account, or such multiple visits to a performers website, mayindicate affinity for the performer at least because such use shows thatthe potential ticket purchaser has a history of listening to the band'smusic and/or visiting the performer's website.

In some circumstances, an API may be administered by automated softwareassociated with a ticket-buying entity such that the entity utilizes theAPI to purchase tickets. Such an API may be designed to interface with aticket-selling entity. Such automated software may be programmed to usea custom-designed API to purchase either as many tickets as possible, orthe highest quality tickets, or some other suitable parameter associatedwith ticket purchasing. Such automated software may be utilized byticket resellers, commonly referred to as “scalpers” or “touts,” toincrease the success rate, with respect to quality and/or quantity, ofpurchasing tickets. Such automated software may increase the successrate of ticket purchasing at least because it is automated and providesquicker reaction time to queues delivered by ticket sellers and/ortiming of ticket sales.

In some embodiments, a band may wish to populate selected, preferablydesirable, seats for a concert with loyal participants. Accordingly,such a band may award concert ticket purchasing priority to certainloyal participants based on identifiable indications of loyaltyassociated with the social media IDs of such loyal participants. Asdescribed above, this process may be referred to herein as socialqueuing. These, or other, embodiments may include restricting automatedaccounts to purchasing less desirable seats. In certain embodiments, atime delay in the queue may allow for the queue operator to operate andassign event ticket purchase opportunities.

Certain embodiments may not use a queue at all to assign event ticketpurchase opportunities but rather may immediately assign ticket purchaseopportunities in response to the characterization of the user based oninformation keyed to the user's social media ID or other suitableinformation.

Certain embodiments may use multiple queues to assign event ticketpurchase opportunities. In certain embodiments, each queue may beprioritized, with respect to the other queues, based on the index scoreof the members of the queue. In certain embodiments, the members of thequeue may be rated based on the timestamp of the attempted ticketpurchase by the member of the queue and/or in combination with the indexscore of the member.

Step 414 shows the above-described factor for determining the level ofpotential ticket purchaser interest in the band. Specifically, step 414shows performing analysis of account history associated with socialmedia ID to determine validity of ID—e.g., is the social media IDassociated with a human being or is it associated with automatedsoftware. In other words, does the account activity associated with thesocial media ID indicate that the social media ID is controlled byautomated software. Social queuing as disclosed herein may preferably beused to determine whether the social media ID, and login associatedtherewith, is controlled by automated software.

FIG. 5 shows an illustrative flow diagram of index generation accordingto certain embodiments. Step 502 shows, for each social media ID and/orthird party data source, performing one or more algorithms to obtain apreferably dynamic index value. The algorithms may contain one or moreof optional steps 504-512 or other suitable steps.

Step 504 shows the exemplary step of allocating one point (or othersuitable quantum) to a social media ID and/or third party data sourceidentifier when the social media account history indicates an expressionof affinity for the band. Such expressions may include a finding of“love” or “like” within a pre-determined number of words of the band.Alternative determinants of affinity may also be utilized.

Step 504 also shows allocating an additional point (or other suitablequantum) to the social media ID and/or third party data sourceidentifier when the expression occurred within a threshold amount oftime with respect to the date for which the tickets are being sold. Forexample, if, once a showing of affinity has been confirmed, then theshowing of affinity may be given an extra point (or other suitablequantum) if the showing of affinity occurred within six (6) months ofthe prospective date for the concert for which the tickets are beingoffered.

Step 506 shows yet another exemplary step of allocating one point (orother suitable quantum) to the social media ID and/or third party datasource identifier when other history linked to the social media account,such as a streaming audio account history—e.g., a Spotify™history—indicates streaming audio account use of the band's music. Suchan indicator may be binary—e.g., if the person has listened to the bandon his or her streaming audio account then the system allocates apoint—or the indicator may require traversal of a threshold—e.g., if theperson has listened to the band more than a threshold amount of times,the threshold amount of times, in one exemplary embodiment, being spreadover a pre-determined period—then the system allocates a point (or othersuitable quantum).

Step 508 shows allocating a point (or other suitable quantum) to thesocial media ID and/or third party data source identifier when thecharacteristics of the social media ID account history and/or thirdparty data source identifier history indicate human ownership andcontrol of the social media ID account and/or account associated withthe third party data source identifier. For example, if the identifierhas less than a pre-determined number of “friends” links, than it may beassumed that the identifier is fraudulent and in fact is an identifiergenerated by automated software or is in some other way acomputer-related entity. In certain embodiments, when an identifier hasbeen determined to be associated with automated software or othercomputer-related-entity then such an identifier may preferably berelegated to purchase tickets in certain sub-optimal seating areas orcompletely barred from purchasing tickets.

In certain embodiments, friends identified in connection with a user'ssocial media ID or other suitable identification information maypreferably be tested to determine whether the friends are genuine orsimulated friend accounts.

In certain embodiments, entire communities of “friendly” accounts,whether social media accounts or other third party data source accounts,may be created to interact with one another in order to generate“friends” on the accounts. Such activity may be used to simulate humanaccount attributes. It should be noted that such activity may also bemeasurable at least in part by measuring the commonality of theinteractions between various members of groups. More specifically, suchcommonality may be simulated and/or detected by repeated communicationsbetween certain friends and/or higher than average level per unit timeof communications among certain groups of friends.

Step 510 shows tabulating an index based, at least in part, on one ormore of steps 504-508. Such tabulation may include adding all the points(or other suitable quanta) accrued in one or more of steps 504-508.Following the tabulation (or other suitable calculation), the sum of thepoints (or other suitable quanta) accrued may be compared to a thresholdvalue. In certain embodiments, if the sum of the points (or othersuitable quanta) is equal to or greater than the threshold value, thenthe identifier may be awarded priority ticket purchasing opportunities.Such priority ticket purchasing opportunities may include more optimalseating, lower prices on seats, first choice at certain seats and/orfirst choice at ticket purchasing.

FIG. 6 shows an illustrative flow diagram of filtering and sortingticket purchasing opportunities based on the index. Step 602 showsdetermining whether the index value is above a pre-determined thresholdvalue (similar to the portion of the specification above correspondingto step 510 in FIG. 5.)

Step 604 shows, when the index value is above the pre-determinedthreshold value, tagging the social media ID as a priority ID withrespect to the band. Step 606 shows receiving logins for attemptedpurchases of concert tickets. Following step 606, the system may sortthe attempted purchase requests based on the presence or absence ofpriority IDs. It should be noted that the determination as to whetherthe logins include priority IDs should preferably be formulated at leastsome pre-determined amount of time prior to receiving purchase requests.Accordingly, for a party to receive priority treatment regarding ticketpurchasing, it may have to pre-register at least some pre-determinedamount of time prior to when the tickets are offered for sale.

Step 608 shows that, for logins, and attempted purchase requestspursuant thereto, associated with priority IDs, the system may placesuch attempted purchase requests in the priority ticket purchase queue.Step 610 shows that, for logins not associated with priority ID, thesystem may place such logins, and associated ticket purchase requests,in general ticket purchase queue. Step 612 shows that, preferablyfollowing receipt of priority ticket purchase requests, such requestsare processed—i.e., seats are sold to the priority ticket purchaserequests on a first-come, first-served basis.

In certain embodiments, an automated ticket reseller may have simulateda human ticket purchaser too well. Such an automated ticket reseller mayscore higher than humanly possible in the formulation of an index. Assuch, certain embodiments may indicate that when the index value isabove a second pre-determined threshold value, the social media ID maybe tagged as a non-priority ID with respect to the band.

FIG. 7 shows an illustrative hybrid flow diagram of filtering andsorting ticket purchasing opportunities based on the index and theoutcome of such filtering and sorting as expressed in event seatingmaps. Steps 706-710 are similar to steps 606-610. Specifically, step 706shows receiving logins for attempted purchase of concert tickets.Following step 706 the system may sort the attempted purchase requestsbased on the presence or absence of priority IDs. Step 708 shows that,for logins, and attempted purchase requests pursuant thereto, associatedwith priority IDs, the system may place such attempted purchase requestsin the priority ticket purchase queue. Step 710 shows that, for loginsnot associated with priority ID, the system may place such logins, andassociated ticket purchase requests, in general ticket purchase queue.

Event ticketing map 712 shows that, preferably following receipt ofpriority ticket purchase requests, such requests are processed such thatmore desirable seats in section A are available to priority ticketpurchase requests. Event ticketing map 714 shows that less desirableseat in section B are available to non-priority ticket purchaserequests.

FIG. 8 shows yet another illustrative hybrid flow diagram of filteringand sorting ticket purchasing opportunities based on the index. In fact,all of elements 802, 804, 806, 808, 810 and 812 correspond to similarelements in FIG. 6. The only difference between FIG. 6 and FIG. 8 isthat FIG. 8 incorporates another path, 811, in the sorting step. WhereasFIG. 6 included a binary decision associated with a determination as towhether the login was a priority login or non-priority, FIG. 8illustrates that systems and methods according to the invention maypreferably include various paths.

For example, FIG. 8 preferably illustrates that embodiments maypreferably include multiple levels of priority, as shown by the ellipsesbetween 808 and 810 as well as the new path 811. Such levels maypreferably be determined based on different threshold levels of indexscoring. Step 811 may preferably identify logins associated withrecognizably non-human purchasing agents and flag such logins forspecial treatment as described herein.

It should be noted that the various embodiments identified herein may beused together. It should be noted that the various embodimentsidentified herein may partially be used together such that one or morefeatures identified in connection with a single embodiment may be usedin connection with one or more other embodiments identified herein. Itshould be noted further that the order of steps of any method claim ordescription of method herein should be understood, unless expresslystated to the contrary, to not be limited to any particular order butrather should be understood to include any order that is both logicallyand grammatical correct.

Thus, methods and apparatus for leveraging social queuing to facilitateevent ticket distribution and to identify and/or prevent ticketpurchaser simulation have been provided. Persons skilled in the art willappreciate that the present invention can be practiced in embodimentsother than the described embodiments, which are presented for purposesof illustration rather than of limitation, and that the presentinvention is limited only by the claims that follow.

What is claimed is:
 1. A computer-implemented method for identifying asimulated user third party data source account history, the methodcomprising: querying a third: party data source to obtain userinformation; receiving user information associated with a user from thethird-party data source; compiling the user information from thethird-party data source to form a user account history; determiningwhether the user account history includes one or more parameters thatindicate whether the user account history is more similar to a userprofile associated with an automated entity or a user profile associatedwith a human entity; assigning a first set of pre-determined ticketpurchasing opportunities to the user if the user account history isdetermined as being more similar to a user profile associated with anautomated entity; and assigning a second set of pre-determined ticketpurchasing opportunities to the user if the user account history isdetermined as being more similar to a user profile associated with ahuman entity.
 2. The computer-implemented method of claim 1, wherein theone or more parameters includes a determination of less than a thresholdnumber of friends associated with the account.
 3. Thecomputer-implemented method of claim 1, wherein the one or moreparameters includes at a determination more than a threshold frequencyof historic ticket purchases per unit time.
 4. The computer-implementedmethod of claim 1, wherein the one or more parameters includes adetermination of a disparity of location of historic ticket purchasesper unit time.
 5. The computer-implemented method of claim 1, whereinthe one or more parameters includes a historic record of less than athreshold average reaction time to ticket offers.
 6. Thecomputer-implemented method of claim 1, further comprising monitoringthe one or more parameters over a pre-determined period of time.
 7. Thecomputer-implemented method of claim 1, wherein the one or moreparameters includes ticket purchasing history over a pre-predeterminedperiod of time.
 8. The computer-implemented method of claim 1, whereinthe one or more parameters includes performance attendance over apre-determined amount of time.
 9. A computer-implemented method foridentifying a simulated social media account history or a simulatedthird party data source profile, the method comprising: querying asocial media account associated with a user to obtain social mediaaccount information or a third party data source to obtain userinformation; receiving social media account information from the socialmedia account or receiving user information from the third party datasource; compiling either or both of the social media account informationfrom the social media account or the user information from the thirdparty data source to form a user account history; determining whetherthe user account history includes one or more parameters that indicatewhether the user account history is related to an automated entity or ahuman entity; assigning a first set of pre-determined ticket purchasingopportunities to the user if the user account history is determined asbeing related to an automated entity; and assigning a second set ofpre-determined ticket purchasing opportunities to the user if the useraccount history is determined as being related to a human entity. 10.The computer-implemented method of claim 9, wherein the one or moreparameters include a determination of less than a threshold number offriends associated with the account.
 11. The computer-implemented methodof claim 9, wherein the one or more parameters include at adetermination more than a threshold frequency of historic ticketpurchases per unit time.
 12. The computer-implemented method of claim 9,wherein the one or more parameters include a determination of adisparity of location of historic ticket purchases per unit time. 13.The computer-implemented method of claim 9, wherein the one or moreparameters include a historic record of less than a threshold averagereaction time to ticket offers.
 14. The computer-implemented method ofclaim 9, wherein the one or more parameters are monitored over apre-determined period of time.
 15. The computer-implemented method ofclaim 9, wherein the one or more parameters include ticket purchasinghistory over a pre-predetermined period of time.
 16. Thecomputer-implemented method of claim 9, wherein the one or moreparameters include historical performance attendance over apre-determined amount of time.
 17. An article of manufacture comprisinga non-transitory computer usable medium having computer readable programcode embodied therein, the code when executed by one or more processorsfor configuring a computer to execute a method to identify automatedticket purchasers, the method comprising: using a receiver to receive aplurality of logins, each of the logins intended for an event ticketpurchase, each of the logins corresponding to user information; using aprocessor to retrieve an account history associated with the userinformation; using the processor to calculate an index value based onthe user information, said index value being based, at least in part, onthe retrieved account history; using the processor to use an algorithmto categorize a first portion of the plurality of logins as human-basedlogins and a second portion of the plurality of logins asnon-human-based logins; and following a pre-determined amount of timeafter the categorizing, substantially simultaneously replying to atleast portion of the plurality of logins, said replying comprisingproviding a first set of pre-determined ticket purchasing opportunitiesto the human-based logins and a second set of pre-determined ticketpurchasing opportunities to the non-human-based logins.
 18. The methodof claim 17, wherein the plurality of logins are received at least 10minutes prior to the replying.
 19. The method of claim 17, furthercomprising using the processor to use a second algorithm to categorize afirst portion of the plurality of logins as human-based logins, a secondportion of the plurality of logins as non-human-based logins and a thirdportion of the plurality of logins as human-based priority logins. 20.The method of claim 19 wherein replying comprising providing a first setof pre-determined ticket purchasing opportunities to the human-basedlogins, a second set of pre-determined ticket purchasing opportunitiesto the non-human-based logins and a third set of pre-determined ticketpurchasing opportunities to the human-based priority logins.